Abstract

Addressing sustainability requires developing analytical methods that minimize hazards, waste, and energy utilization per the principles of green and white chemistry. Herein, we present a sustainable spectrophotometric chemometrics technique for quantifying the recently approved Bupivacaine (BUP) and Meloxicam (MEL) combination and the potential BUP carcinogen, 2,6-dimethylaniline (DMA). Our models, including partial least squares (PLS), principal component regression (PCR), genetic algorithm-PLS (GA-PLS), and GA-PCR, were established through a comprehensive experimental design involving 25 mixtures as the calibration set. A key innovation is the utilization of the Latin Hypercube Sampling (LHS) technique, which enables the creation of a robust validation set for evaluating the performance and generalizability of these models. The GA-PLS model demonstrated excellent accuracy, with recovery percentages (R%) from 98 to 102% for all analytes, and root mean square error of calibration (RMSEC) and prediction (RMSEP) of (0.097, 0.050, and 0.112) and (0.119, 0.044 and 0.131) for BUP, DMA, and MEL, respectively. The model also showed a negligible bias-corrected mean square error of prediction (BCMSEP) of (-0.014, −0.003, and 0.018), with relative root mean square error of prediction (RRMSEP) reaching (0.992, 0.752, and 0.659), and limits of detection (LOD) reaching (0.153, 0.081, and 0227) for BUP, DMA, and MEL, respectively. Comprehensive greenness, blueness, and whiteness assessments were performed and compared between the suggested and reported methods. This research pioneers a green–blue-white alternative to conventional approaches, serving as a model for developing sustainable techniques that reduce the usage of resources and chemical waste while meeting the global appeal for environmentally accountable solutions.

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